Human-Centered Multi-Sensor Framework for Identifying Driving Patterns Associated with Cognitive Decline Through Quantitative Analysis
DOI: 10.21203/rs.3.rs-9131446/v1
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Summary
This paper presents a pilot proof-of-concept framework for detecting Mild Cognitive Impairment (MCI) in older adults by analyzing naturalistic driving behavior through a multi-sensor telematics system. Motivated by the lack of standardized, real-world methods to assess driving fitness in cognitively declining populations, the study aims to identify specific driving patterns associated with MCI using unobtrusive in-vehicle sensing. The research addresses gaps in prior studies, which often relied on single analytical methods or failed to account for participant-level pseudoreplication. The study deployed AutoPi telematics units, integrating GPS, inertial measurement units (IMU), and OBD-II sensors, in the vehicles of 51 older adult drivers (aged 65+) in South Florida over a 28-month period. The cohort included 10 participants with MCI and 41 cognitively unpaired individuals, yielding 20,145 trips. Clinical diagnoses were confirmed via Clinical Dementia Rating scores and neuropsychological assessments. The analytical pipeline processed 36 driving behavior indices, including demographic factors and sensor-derived metrics for exposure, kinematics, and vehicle performance. To prevent pseudoreplication, all supervised modeling was conducted at the participant level. The methodology involved multicollinearity reduction using Variance Inflation Factor (VIF), K-Means clustering for behavioral profiling, Random Forest feature ranking, and Welch’s t-tests with Benjamini-Hochberg correction. Predictive modeling utilized L1-regularized logistic regression with leave-one-out cross-validation (LOOCV) and Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. The full model achieved an Area Under the Curve (AUC) of 0.698 (95% CI: 0.493–0.872) with 80% sensitivity and 66% specificity. Throttle position variability and mean throttle application emerged as the strongest sensor-derived predictors (Cohen’s d = 0.86), reflecting impaired speed regulation consistent with executive dysfunction. However, the model’s discrimination was heavily influenced by gender, as 9 of the 10 MCI participants were female. A sensitivity analysis excluding gender reduced the AUC to 0.598, comparable to a telematics-only model (AUC = 0.595), indicating that the driving-behavior signal is meaningful but modest when demographic confounding is removed. Cold-start analysis determined that approximately 50 trips, representing roughly four months of driving, constitute the minimum viable observation window for reliable screening. The findings support telematics-based monitoring as a promising framework for MCI detection but highlight significant limitations regarding cohort composition. The substantial performance disparities driven by gender imbalance underscore the necessity for validation in larger, gender-balanced cohorts before clinical deployment. The study establishes a methodologically rigorous, sensor-agnostic framework that prevents pseudoreplication and provides specific quantitative thresholds for observation windows, offering a foundation for future research into unobtrusive cognitive assessment tools.
Key finding
Throttle position variability and mean throttle application are the strongest sensor-derived predictors of Mild Cognitive Impairment, though overall model discrimination is substantially influenced by gender confounding.
Methodology
naturalistic
Sample size: 51
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | crossref | — | — | 2 | 2026-06-04 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- mci dementia driving
- cognitive impairment
- telematics crash prediction
- exposure measurement
- cognitive capacity variation
- drowsiness detection algorithms
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Methodological Resource: validation psychometrics, tool software
- Theoretical Contribution: computational model